Home / Blog / May 2013 / Data Collection and Analysis for the Regional Indicators Initiative

Data Collection and Analysis for the Regional Indicators Initiative

Posted by Anna Jursik | Date May 28, 2013

Earlier this spring, LHB’s Rick Carter guest blogged about the Regional Indicators Initiative, a data collection and carbon tracking project for Minnesota cities. I sat down with him to learn more about data collection strategies, underlying assumptions, and how other regions could track metrics for sustainability.

PART 1

Anna: What were some of the challenges of nailing down which metrics were measurable?

Rick:

From early on, we considered waste, travel and non-travel energy, and water. Water and energy took on lives on their own and we were able to collect them by residential and commercial/industrial. Travel was already substantially measured by the state, so it was just a matter of harvesting that data and understanding it better.

We took a more rigorous approach going back to ask “now that we’ve established four metrics, which actions either taken by cities in general or by specific cities would affect the numbers?” We did a quick study of what data thought we could get and what the highest level of interaction would be, then performed a cross-check.

Anna: Did you consider collecting and including other metrics to correlate with other GreenStep Cities best practices?

Rick:

We’ve been capturing a whole set of readily available parallel data, including the area of each city, population, job counts, precipitation in inches, heating degree days, and cooling degree days.

Other groups have suggested additional metrics. Wastewater is one. Storm water is interesting, but I’m not sure it’s measurable. We haven’t found another metric that we felt was crucial, connected to GreenStep, and relatively easy to measure at the city scale. That’s not to say there aren’t, they just are not obvious.

Anna: Why did you decide to include waste as a metric?

Rick:

We picked metrics we could measure at a city scale with relative ease and had a substantial impact. Energy, travel, and water were clear choices. To calculate the carbon baseline using the ICELI Protocol, which is the current international standard, you must include waste, even though it’s a small slice of that carbon pie. The data is only available by county, so the standard allows you to prorate it.

Anna: Do you know of other ways to calculate consumption and embodied resources in consumer goods?

Rick:

The carbon implications of waste in our study include the direct handling of the waste and putting it in the landfill. But if you think about it, the complete lifecycle of everything going into the landfill contributes. Waste is a red herring in the way that it has all those implications. And it strikes me how so many sustainability plans--whether for a smaller organization like a company or a set of organizations like a city or a state--include a big section about waste, even though it represents a very small part of the carbon contribution.

Anna: I’m always curious about that.

Rick:

I think it’s in large part because we are connected to waste. We handle it and hold it and see it.

Anna: Whereas energy is less tangible.

Rick:

Yes, much less tangible. And maybe also because the more common elements of our environmental movement have focused on waste: pick up trash. Recycle. Those are more familiar actions.

Anna: You mentioned ICLEI as the current international standard.

Rick:

On the community scale, ICLEI is the commonly accepted protocol internationally. During our project, they rolled out their new methodology, so we redid all our calculations. That’s a lot of time spent on something that changes so subtly. But the protocol is very important to the City of Minneapolis. It’s not about comparing themselves to Maplewood or Falcon Heights, but to Seattle or Singapore or Stockholm. For credibility’s sake, they want to know that the same methodology is used.

A defining feature of the RII study is that we primarily focus on energy in BTUs, travel in miles, water in gallons, and waste in pounds. The more common way to measure these things is a rolled-up carbon metric of tons equivalent.

Anna: Would you say that the field is moving towards a common process to calculate carbon footprints?

Rick:

There are three main methodologies: geographic, transboundary, and consumption.

RII uses a geographic methodology, which a lot of people argue is limited and not necessarily fair. We essentially draw a line around the city, measure the things inside that boundary, and calculate their carbon output. Our numbers average around 18 tons per person per year.

When our study measures a trip, someone driving from A to B, using this geographic method; we measure the total miles inside the city. The transboundary method uses a very complex methodology to attribute chunks of the trip in different areas based on different percentages. And it adds other things like airline travel. That’s hard to explain to a city but there is a protocol. With the transboundary method, the output numbers are around 19 to 20 tons.

The researchers say that the consumption-based method is the most rigorous. That methodology considers the carbon implications of all an individual’s actions, between the driving of his car and the heating of his house and the buying of his shoes. Those numbers get up to 25 tons per person per year, which is more true in terms of measuring carbon.

Anna: Would you say that the geographic method reflects the travel activities of the city’s residents, or its investments in transportation infrastructure?

Rick:

Our ultimate goal is to help cities understand what to do. If I report the energy use of this building, the people who own and occupy it could take actions to reduce consumption and see the change. So how can a city encourage a person to take fewer trips or drive fewer miles? We don’t know in the same way as reducing energy in a building. If we were going to do one thing differently in terms of our carbon calculation, it would be travel. That could provide a better way of understanding what could be done.

A perfect example is the cities that will ultimately be on the currently planned light rail lines. Theoretically, that would impact the city’s travel over time. It’s hard to think about light rail as something that a city does, or as related back to a city. It’s more of a regional pattern. So it will be interesting to see what the changes in the cities that are on the lines versus the cities that are off the lines.

Anna: What were the major challenges in obtaining the different end use energy data?

Rick:

In the beginning, we stumbled by asking the utilities for the data ourselves. They gave us no response, or definite no’s. After a few months, we had an ah-ha moment: the cities needed to ask the utility companies. We drafted a template letter asking each city’s utility company to verify service and provide residential consumption data separate from commercial/industrial, and each district energy provider for their total output in either therms, kW, or pounds. We gave that form letter to each of the cities and maintained a spreadsheet with each city and each of their utilities, to track the dates that they sent their letters and the dates that the data came back to us.

Most of the utility companies were cooperative. There were a couple little oddball glitches. In one community there had been a change: a portion of their city had been served by a muni and now was served by an investor-owned utility. So receiving partial data that varied from year to year over the four years got to be tricky. But it was very much the exception.

Anna: That’s encouraging for other people working on similar initiatives.

Rick:

Yes, but we still have a few hurdles to overcome.

For example, Xcel Energy operates with the 15/15 rule for data aggregation. When they give anyone a dataset, in our case they give us a sheet: one year for one city. If there are less than fifteen users in any premise type, they are excluded from the dataset. For example, if ten households in a small community sign up to pay for wind-source, their consumption is excluded. And if any one user in that utility type, in that dataset, uses more than 15% of the energy, Xcel excludes them. The standard is to protect consumer privacy, so Xcel does not name the excluded consumers.

We’re trying to figure out a way to work around it by the way that we ask for the info. It doesn’t vary the data, but consistently impacts each city it affects over the four years. Although we don’t even know that: one user could use 14% of the energy one year and 16% the next.

Anna: And so they wouldn’t appear in the second year’s dataset?

Rick:

Right. We found out about the standard because we had already obtained 2008 and 2009 data for one of the pilot cities. When we went back and had all twenty cities ask for four years of data, that city’s new data was substantially different than the old information from those years.

So that’s an example of a challenge. In a couple other instances, we received information, data that came back. One utility company had a major pattern of inconsistency, where multiple cities had drastically more or less usage in the first two years than the second two years, with the same premise counts. Once we started putting these numbers in charts and graphs, it became pretty obvious. After we asked several times, they acknowledged they had a problem, but it took them months to get the reworked data.

Anna: Why is it important to normalize by jobs, population, households, and weather? Did you think about other factors to consider?

Rick:

Some people like to see the information normalized. It helps when comparing one city to another or a group of cities to a different group of cities. Someone may ask, for example, “is it really fair to compare this city to that city? Because one grew a lot and the other didn’t.” In that case, she could take the residential energy consumption and compare it on a per-person or per-household basis.

It’s useful but not necessarily critical. To me, total energy use is the pattern we’re trying to reduce. Straight up. I see two ways to mitigate climate change. One way is to make the grid cleaner in order to provide the same number of BTUs with less carbon output. Many people are working on that, and we’re making considerable progress but not enough. The other important way is to reduce the consumption number, whatever the baseline.

Anna: Do you have any advice for other metro areas planning similar initiatives?

Rick:

The first thing is to be organized. We did it in an ad hoc way. Looking back, I don’t know that we could have done anything differently. But if a new region were going to start, I would suggest having the Met Council or the State or the counties ask for the information. Make fewer requests and specify exactly how you want the information, instead of naming the output you want and letting each utility company do it a certain way.

Also, consider using practices that allow us to share the information from region to region. Not necessarily using all the the same methods as we did in our project. But for example, rather than calculations in therms/household and kW/household, work in btus/household so we can compare from region to region.